IPhone Sales Analysis¶

In [2]:
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
In [3]:
data = pd.read_csv("apple_products (1).csv")
In [4]:
data
Out[4]:
Product Name Product URL Brand Sale Price Mrp Discount Percentage Number Of Ratings Number Of Reviews Upc Star Rating Ram
0 APPLE iPhone 8 Plus (Gold, 64 GB) https://www.flipkart.com/apple-iphone-8-plus-g... Apple 49900 49900 0 3431 356 MOBEXRGV7EHHTGUH 4.6 2 GB
1 APPLE iPhone 8 Plus (Space Grey, 256 GB) https://www.flipkart.com/apple-iphone-8-plus-s... Apple 84900 84900 0 3431 356 MOBEXRGVAC6TJT4F 4.6 2 GB
2 APPLE iPhone 8 Plus (Silver, 256 GB) https://www.flipkart.com/apple-iphone-8-plus-s... Apple 84900 84900 0 3431 356 MOBEXRGVGETABXWZ 4.6 2 GB
3 APPLE iPhone 8 (Silver, 256 GB) https://www.flipkart.com/apple-iphone-8-silver... Apple 77000 77000 0 11202 794 MOBEXRGVMZWUHCBA 4.5 2 GB
4 APPLE iPhone 8 (Gold, 256 GB) https://www.flipkart.com/apple-iphone-8-gold-2... Apple 77000 77000 0 11202 794 MOBEXRGVPK7PFEJZ 4.5 2 GB
... ... ... ... ... ... ... ... ... ... ... ...
57 APPLE iPhone SE (Black, 64 GB) https://www.flipkart.com/apple-iphone-se-black... Apple 29999 39900 24 95909 8161 MOBFWQ6BR3MK7AUG 4.5 4 GB
58 APPLE iPhone 11 (Purple, 64 GB) https://www.flipkart.com/apple-iphone-11-purpl... Apple 46999 54900 14 43470 3331 MOBFWQ6BTFFJKGKE 4.6 4 GB
59 APPLE iPhone 11 (White, 64 GB) https://www.flipkart.com/apple-iphone-11-white... Apple 46999 54900 14 43470 3331 MOBFWQ6BVWVEH3XE 4.6 4 GB
60 APPLE iPhone 11 (Black, 64 GB) https://www.flipkart.com/apple-iphone-11-black... Apple 46999 54900 14 43470 3331 MOBFWQ6BXGJCEYNY 4.6 4 GB
61 APPLE iPhone 11 (Red, 64 GB) https://www.flipkart.com/apple-iphone-11-red-6... Apple 46999 54900 14 43470 3331 MOBFWQ6BYYV3FCU7 4.6 4 GB

62 rows × 11 columns

In [5]:
print(data.isnull().sum())

Product Name           0
Product URL            0
Brand                  0
Sale Price             0
Mrp                    0
Discount Percentage    0
Number Of Ratings      0
Number Of Reviews      0
Upc                    0
Star Rating            0
Ram                    0
dtype: int64
In [6]:
print(data.describe())

          Sale Price            Mrp  Discount Percentage  Number Of Ratings  \
count      62.000000      62.000000            62.000000          62.000000   
mean    80073.887097   88058.064516             9.951613       22420.403226   
std     34310.446132   34728.825597             7.608079       33768.589550   
min     29999.000000   39900.000000             0.000000         542.000000   
25%     49900.000000   54900.000000             6.000000         740.000000   
50%     75900.000000   79900.000000            10.000000        2101.000000   
75%    117100.000000  120950.000000            14.000000       43470.000000   
max    140900.000000  149900.000000            29.000000       95909.000000   

       Number Of Reviews  Star Rating  
count          62.000000    62.000000  
mean         1861.677419     4.575806  
std          2855.883830     0.059190  
min            42.000000     4.500000  
25%            64.000000     4.500000  
50%           180.000000     4.600000  
75%          3331.000000     4.600000  
max          8161.000000     4.700000

top 10 iphones sold in india¶

In [7]:
highest_rated = data.sort_values(by =["Star Rating"], ascending= False )
highest_rated = highest_rated.head(10)
print(highest_rated['Product Name'])

20     APPLE iPhone 11 Pro Max (Midnight Green, 64 GB)
17         APPLE iPhone 11 Pro Max (Space Grey, 64 GB)
16    APPLE iPhone 11 Pro Max (Midnight Green, 256 GB)
15               APPLE iPhone 11 Pro Max (Gold, 64 GB)
14              APPLE iPhone 11 Pro Max (Gold, 256 GB)
0                    APPLE iPhone 8 Plus (Gold, 64 GB)
29                     APPLE iPhone 12 (White, 128 GB)
32          APPLE iPhone 12 Pro Max (Graphite, 128 GB)
35                     APPLE iPhone 12 (Black, 128 GB)
36                      APPLE iPhone 12 (Blue, 128 GB)
Name: Product Name, dtype: object

lets have a look at the number of ratings of the highest rated iphone on flipkart¶

In [8]:
iphones = highest_rated["Product Name"].value_counts()
labels = iphones.index
counts = highest_rated["Number Of Ratings"]
figure = px.bar(highest_rated, x=labels, y=counts,
                title = "Numer of rating of highest rated iphones")
figure.show()
In [9]:
iphones
Out[9]:
Product Name
APPLE iPhone 11 Pro Max (Midnight Green, 64 GB)     1
APPLE iPhone 11 Pro Max (Space Grey, 64 GB)         1
APPLE iPhone 11 Pro Max (Midnight Green, 256 GB)    1
APPLE iPhone 11 Pro Max (Gold, 64 GB)               1
APPLE iPhone 11 Pro Max (Gold, 256 GB)              1
APPLE iPhone 8 Plus (Gold, 64 GB)                   1
APPLE iPhone 12 (White, 128 GB)                     1
APPLE iPhone 12 Pro Max (Graphite, 128 GB)          1
APPLE iPhone 12 (Black, 128 GB)                     1
APPLE iPhone 12 (Blue, 128 GB)                      1
Name: count, dtype: int64
In [10]:
iphones = highest_rated["Product Name"].value_counts()
labels = iphones.index
counts = highest_rated["Number Of Reviews"]
figure = px.bar(highest_rated, x=labels, y=counts,
                title = "Numer of reviews of highest rated iphones")
figure.show()
In [13]:
figure = px.scatter(data_frame = data, x ="Number Of Ratings",
                   y = "Sale Price", size = "Discount Percentage", trendline="ols",
                   title = "Relationship between Sales price and Number of rating")
figure.show()
In [12]:
figure = px.scatter(data_frame = data, x = "Number Of Ratings",
                    y ="Discount Percentage", size ="Sale Price", trendline ="ols",
                   title = "Relationship between Disscount Percentage and Number of rating")
figure.show()
In [ ]: